Alphavirus in Panama

FOI models fitted to age-structured sero-prevalence

# ---- Datasets
dat1 <- readRDS('data/Panama2012.RDS') %>% mutate(PlaceName = 'Darien',  id = stringr::str_sub(dataset_id, 1, 3))
dat2 <- readRDS('data/Panama2017.RDS') %>% mutate(PlaceName = 'Mogue',  id = stringr::str_sub(dataset_id, 1, 3))
dat3 <- readRDS('data/Panama2012_places.RDS')%>% mutate(survey = dataset_id)


dat0   <- rbind(dat3, dat1, dat2) %>% filter(id %in% c('005', '010'))
dat0$virus[dat0$virus=='UNA'] <- 'UNAV' 
dat0 <- dat0 %>%  mutate(counts = pos,
                         survey = paste(virus,tsur, PlaceName, id)) %>%
  arrange(desc(survey)) %>%  mutate(age_mean_f = floor((age_min + age_max)/2))

(datasets <- unique(dat0$survey))








for (s in datasets) 
{
  
  
  dat <- filter(dat0, survey == s) %>% arrange(age_mean_f) %>%
    mutate(birth_year = tsur - age_mean_f)
  
  res  <- fFitModel(model_d, dat)
  loo_res <- loo::loo(res$fit, save_psis = TRUE, 'logLikelihood')
  plot_res <- fPlotModel(res, dat, 'model dec', 'Student_t')
  
  res_survey <- list(dat  = dat,
                     res  = res,
                     loo_res  = loo_res,
                     plot_res = plot_res)
  
  saveRDS(res_survey, paste0('res/mod_decs', s, '.RDS' ))
  print(plot_res)
  
  rm(dat, res, loo_res, plot_res, res_survey)
}